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<a href="_r_b_f_layer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief      Implements a radial basis function layer.</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * </span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * \author      O. Krause</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \date        2014</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> *</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * </span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * </span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> *</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> */</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#ifndef SHARK_MODELS_RBFLayer_H</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#define SHARK_MODELS_RBFLayer_H</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="_d_l_l_support_8h.html">shark/Core/DLLSupport.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_model_8h.html">shark/Models/AbstractModel.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#include &lt;boost/math/constants/constants.hpp&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment"></span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">///  \brief Implements a layer of radial basis functions in a neural network.</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">/// </span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">/// A Radial basis function layer as modeled in shark is a set of N</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">/// Gaussian distributions \f$ p(x|i) \f$.</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///   p(x|i) = e^{\gamma_i*\|x-m_i\|^2}</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// and the layer transforms an input x to a vector \f$(p(x|1),\dots,p(x|N)\f$.</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">///  The \f$\gamma_i\f$ govern the width of the Gaussians, while the</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///  vectors \f$ m_i \f$ set the centers of every Gaussian distribution. </span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// RBF networks profit much from good guesses on the centers and</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// kernel function parameters.  In case of a Gaussian kernel a call</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// to k-Means or the EM-algorithm can be used to get a good</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// initialisation for the network.</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">///</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// \ingroup models</span></div>
<div class="foldopen" id="foldopen00057" data-start="{" data-end="};">
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html">   57</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_r_b_f_layer.html" title="Implements a layer of radial basis functions in a neural network.">RBFLayer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_model.html" title="Base class for all Models.">AbstractModel</a>&lt;RealVector,RealVector&gt;</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>{</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    <span class="keyword">struct </span>InternalState: <span class="keyword">public</span> <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>{</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        RealMatrix norm2;</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        </div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        <span class="keywordtype">void</span> resize(std::size_t numPatterns, std::size_t numNeurons){</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>            norm2.resize(numPatterns,numNeurons);</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        }</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    };</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span> </div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">    ///  \brief Creates an empty Radial Basis Function layer.</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#aa6b23dfb2c34ecbb5d96034eb17bd5b6">   70</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#aa6b23dfb2c34ecbb5d96034eb17bd5b6" title="Creates an empty Radial Basis Function layer.">RBFLayer</a>();</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    <span class="comment"></span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">    ///  \brief Creates a layer of a Radial Basis Function Network.</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">    ///  This method creates a Radial Basis Function Network (RBFN) with</span></div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment">    ///  \em numInput input neurons and \em numOutput output neurons.</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment">    ///  \param  numInput  Number of input neurons, equal to dimensionality of</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    ///                    input space.</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">    ///  \param  numOutput Number of output neurons, equal to dimensionality of</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">    ///                    output space and number of gaussian distributions</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#aa6a28b5b0f7b69d9b2a319b1e1e8408e">   81</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#aa6a28b5b0f7b69d9b2a319b1e1e8408e" title="Creates a layer of a Radial Basis Function Network.">RBFLayer</a>(std::size_t numInput, std::size_t numOutput);</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment"></span> </div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00084" data-start="{" data-end="}">
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a5c92a238e03636179012151422f54024">   84</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a5c92a238e03636179012151422f54024" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;RBFLayer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment"></span> </div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">    ///\brief Returns the current parameter vector. The amount and order of weights depend on the training parameters.</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">    ///The format of the parameter vector is \f$ (m_1,\dots,m_k,\log(\gamma_1),\dots,\log(\gamma_k))\f$</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">    ///if training of one or more parameters is deactivated, they are removed from the parameter vector</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a5e089c9692be82ff557922798fecd588">   91</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> RealVector <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a5e089c9692be82ff557922798fecd588" title="Returns the current parameter vector. The amount and order of weights depend on the training paramete...">parameterVector</a>()<span class="keyword">const</span>;</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    <span class="comment"></span></div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment">    ///\brief Sets the new internal parameters.</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a8b8883b0033bb8a18936be6ee378d866">   94</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a8b8883b0033bb8a18936be6ee378d866" title="Sets the new internal parameters.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters);</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    <span class="comment"></span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment">    ///\brief Returns the number of parameters which are currently enabled for training.</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#ae53a34bf645bccbbbc940159401268cc">   97</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> std::size_t <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ae53a34bf645bccbbbc940159401268cc" title="Returns the number of parameters which are currently enabled for training.">numberOfParameters</a>()<span class="keyword">const</span>;</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment"></span> </div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment">    ///\brief Returns the number of input neurons.</span></div>
<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a44b2ae85c21a914c7e82b613fd99b311">  100</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a44b2ae85c21a914c7e82b613fd99b311" title="Returns the number of input neurons.">inputShape</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#a47e3acee6084df0af2c5cfcfd116c57d" title="The center points. The i-th element corresponds to the center of neuron number i.">m_centers</a>.size2();</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    }</div>
</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    <span class="comment"></span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">    ///\brief Returns the number of output neurons.</span></div>
<div class="foldopen" id="foldopen00105" data-start="{" data-end="}">
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a5f0b042b8eaffbd25b1c3b980f0073c5">  105</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_shape.html" title="Represents the Shape of an input or output.">Shape</a> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a5f0b042b8eaffbd25b1c3b980f0073c5" title="Returns the number of output neurons.">outputShape</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#a47e3acee6084df0af2c5cfcfd116c57d" title="The center points. The i-th element corresponds to the center of neuron number i.">m_centers</a>.size1();</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>    }</div>
</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    </div>
<div class="foldopen" id="foldopen00109" data-start="{" data-end="}">
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a3193c0bff83fdf1e5ed37f12d9639351">  109</a></span>    boost::shared_ptr&lt;State&gt; <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a3193c0bff83fdf1e5ed37f12d9639351" title="Creates an internal state of the model.">createState</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        <span class="keywordflow">return</span> boost::shared_ptr&lt;State&gt;(<span class="keyword">new</span> InternalState());</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>    }</div>
</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>    </div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>    <span class="comment"></span></div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">    ///  \brief Configures a Radial Basis Function Network.</span></div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span><span class="comment">    ///  This method initializes the structure of the Radial Basis Function Network (RBFN) with</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment">    ///  \em numInput input neurons, \em numOutput output neurons and \em numHidden</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">    ///  hidden neurons.</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment">    ///  \param  numInput  Number of input neurons, equal to dimensionality of</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment">    ///                    input space.</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment">    ///  \param  numOutput Number of output neurons (basis functions), equal to dimensionality of</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment">    ///                    output space.</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a1218b1268f1cac744be2ac911fce9484">  124</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a1218b1268f1cac744be2ac911fce9484" title="Configures a Radial Basis Function Network.">setStructure</a>(std::size_t numInput, std::size_t numOutput);</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span> </div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    </div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_model.html" title="Base class for all Models.">AbstractModel</a>&lt;RealVector,RealVector&gt;<a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ab37758750b8174cf1d93aa5e90eacef1" title="Standard interface for evaluating the response of the model to a batch of patterns.">::eval</a>;</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#ab37758750b8174cf1d93aa5e90eacef1">  128</a></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ab37758750b8174cf1d93aa5e90eacef1" title="Standard interface for evaluating the response of the model to a batch of patterns.">eval</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_model.html#a518304e95092673b7b6438cace052ef6" title="defines the batch type of the input type.">BatchInputType</a> <span class="keyword">const</span>&amp; patterns, <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#aa0c72e230b9a1324c95ba8ac0b07ba13" title="defines the batch type of the output type">BatchOutputType</a>&amp; outputs, <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>&amp; state)<span class="keyword">const</span>;</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>    </div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span> </div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#ae157c4443c817e640e439081a380c1c9">  131</a></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ae157c4443c817e640e439081a380c1c9" title="calculates the weighted sum of derivatives w.r.t the parameters.">weightedParameterDerivative</a>(</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#a518304e95092673b7b6438cace052ef6" title="defines the batch type of the input type.">BatchInputType</a> <span class="keyword">const</span>&amp; pattern, <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#aa0c72e230b9a1324c95ba8ac0b07ba13" title="defines the batch type of the output type">BatchOutputType</a> <span class="keyword">const</span>&amp; outputs, </div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#aa0c72e230b9a1324c95ba8ac0b07ba13" title="defines the batch type of the output type">BatchOutputType</a> <span class="keyword">const</span>&amp; coefficients, <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state, RealVector&amp; gradient</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    )<span class="keyword">const</span>;</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment"></span> </div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="comment">    ///\brief Enables or disables parameters for learning.</span></div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">    /// \param centers whether the centers should be trained</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span><span class="comment">    /// \param width whether the distribution width should be trained</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#ab5f5fa653d9306ed7f27d415531d1b75">  140</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ab5f5fa653d9306ed7f27d415531d1b75" title="Enables or disables parameters for learning.">setTrainingParameters</a>(<span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ad8f489205e3fb40eb807298df0c4819a" title="Returns the center values of the neurons.">centers</a>, <span class="keywordtype">bool</span> width);</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="comment"></span> </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span><span class="comment">    ///\brief Returns the center values of the neurons.</span></div>
<div class="foldopen" id="foldopen00143" data-start="{" data-end="}">
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#ad8f489205e3fb40eb807298df0c4819a">  143</a></span><span class="comment"></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#a518304e95092673b7b6438cace052ef6" title="defines the batch type of the input type.">BatchInputType</a> <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ad8f489205e3fb40eb807298df0c4819a" title="Returns the center values of the neurons.">centers</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#a47e3acee6084df0af2c5cfcfd116c57d" title="The center points. The i-th element corresponds to the center of neuron number i.">m_centers</a>;</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">    ///\brief Sets the center values of the neurons.</span></div>
<div class="foldopen" id="foldopen00147" data-start="{" data-end="}">
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#af81dd8790d90e312bb3c7e595e86fe3f">  147</a></span><span class="comment"></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#a518304e95092673b7b6438cace052ef6" title="defines the batch type of the input type.">BatchInputType</a>&amp; <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#af81dd8790d90e312bb3c7e595e86fe3f" title="Sets the center values of the neurons.">centers</a>(){</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#a47e3acee6084df0af2c5cfcfd116c57d" title="The center points. The i-th element corresponds to the center of neuron number i.">m_centers</a>;</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    }</div>
</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    <span class="comment"></span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span><span class="comment">    ///\brief Returns the width parameter of the Gaussian functions </span></div>
<div class="foldopen" id="foldopen00152" data-start="{" data-end="}">
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#ae5709681c4970ec1eecfc091ef67a17c">  152</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ae5709681c4970ec1eecfc091ef67a17c" title="Returns the width parameter of the Gaussian functions.">gamma</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#abe31b5060f353d51e3ad91033ea74b93" title="stores the width parameters of the Gaussian functions">m_gamma</a>;</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    <span class="comment"></span></div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment">    /// \brief sets the width parameters - the gamma values - of the distributions.</span></div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a9cd0894ca90ba7ec7c52956c22ada23c">  157</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a9cd0894ca90ba7ec7c52956c22ada23c" title="sets the width parameters - the gamma values - of the distributions.">setGamma</a>(RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#ae5709681c4970ec1eecfc091ef67a17c" title="Returns the width parameter of the Gaussian functions.">gamma</a>);</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    <span class="comment"></span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span><span class="comment">    /// From ISerializable, reads a model from an archive</span></div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a332b99e89c51c3a80da79691f7b878f5">  160</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a332b99e89c51c3a80da79691f7b878f5" title="From ISerializable, reads a model from an archive.">read</a>( <a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp; archive );</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span><span class="comment"></span> </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span><span class="comment">    /// From ISerializable, writes a model to an archive</span></div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a05b2cbad373fbe6e71cae2df22cc6887">  163</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_b_f_layer.html#a05b2cbad373fbe6e71cae2df22cc6887" title="From ISerializable, writes a model to an archive.">write</a>( <a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp; archive ) <span class="keyword">const</span>;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>    <span class="comment">//====model parameters</span></div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span><span class="comment"></span> </div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span><span class="comment">    ///\brief The center points. The i-th element corresponds to the center of neuron number i</span></div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a47e3acee6084df0af2c5cfcfd116c57d">  168</a></span><span class="comment"></span>    RealMatrix <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#a47e3acee6084df0af2c5cfcfd116c57d" title="The center points. The i-th element corresponds to the center of neuron number i.">m_centers</a>;</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    <span class="comment"></span></div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span><span class="comment">    ///\brief stores the width parameters of the Gaussian functions</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#abe31b5060f353d51e3ad91033ea74b93">  171</a></span><span class="comment"></span>    RealVector <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#abe31b5060f353d51e3ad91033ea74b93" title="stores the width parameters of the Gaussian functions">m_gamma</a>;</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span><span class="comment"></span> </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span><span class="comment">    /// \brief the logarithm of the normalization constant for every distribution</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a0542425d6c552464e08a91b36fe869be">  174</a></span><span class="comment"></span>    RealVector <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#a0542425d6c552464e08a91b36fe869be" title="the logarithm of the normalization constant for every distribution">m_logNormalization</a>;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span> </div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>    <span class="comment">//=====training parameters</span><span class="comment"></span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">    ///enables learning of the center points of the neurons</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#ae255d94e66059ddd373c4690aa25f5cc">  178</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#ae255d94e66059ddd373c4690aa25f5cc" title="enables learning of the center points of the neurons">m_trainCenters</a>;<span class="comment"></span></div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span><span class="comment">    ///enables learning of the width parameters.</span></div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"><a class="line" href="classshark_1_1_r_b_f_layer.html#a73ce38fb63627cc5f1ae4ad8c3aae9db">  180</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_r_b_f_layer.html#a73ce38fb63627cc5f1ae4ad8c3aae9db" title="enables learning of the width parameters.">m_trainWidth</a>;</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span> </div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span> </div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span> </div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>};</div>
</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>}</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span> </div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="preprocessor">#endif</span></div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span> </div>
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